双层 RRT* 目标偏差随时运动规划算法

Robotics Pub Date : 2024-03-01 DOI:10.3390/robotics13030041
Hamada Esmaiel, Guolin Zhao, Zeyad A. H. Qasem, Jie Qi, Haixin Sun
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引用次数: 0

摘要

本文提出了一种基于目标偏置的双层结构 RRT* 算法,称为 DOB-RRT*。该算法采用在线优化结构的初始路径进行运动规划。RRT* 的第一层引入了基于反馈的目标偏置策略,并进行分段前向剪枝处理,以快速获得平滑的初始路径。RRT* 的第二层利用初始树结构的启发式方法,通过反向维护策略来优化路径。与传统的 RRT 算法和 RRT* 算法相比,所提出的算法能获得高质量的初始路径,并能在优化过程中快速收敛到渐进最优路径。在真实环境下运行 ROS Kinetic 的实际轮式机器人车辆上,对所提出算法的性能进行了有效评估和实际实验测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Double-Layer RRT* Objective Bias Anytime Motion Planning Algorithm
This paper proposes a double-layer structure RRT* algorithm based on objective bias called DOB-RRT*. The algorithm adopts an initial path with an online optimization structure for motion planning. The first layer of RRT* introduces a feedback-based objective bias strategy with segment forward pruning processing to quickly obtain a smooth initial path. The second layer of RRT* uses the heuristics of the initial tree structure to optimize the path by using reverse maintenance strategies. Compared with conventional RRT and RRT* algorithms, the proposed algorithm can obtain the initial path with high quality, and it can quickly converge to the progressive optimal path during the optimization process. The performance of the proposed algorithm is effectively evaluated and tested in real experiments on an actual wheeled robotic vehicle running ROS Kinetic in a real environment.
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